Update README.md

README.md

@@ -69,3 +69,70 @@ arima_results_df = pd.DataFrame({'Date': prediction_dates, 'Predicted_Price': pr
 print("\nARIMA - 7-Day Forecast")
 print("="*60)
 print(arima_results_df.to_string(index=False))
+
+
+
+<details> <summary>LSTM Model Inference</summary>
+# Install required packages
+!pip install --quiet yfinance joblib tensorflow huggingface_hub scikit-learn
+
+# Import Libraries
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+import joblib
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from sklearn.preprocessing import MinMaxScaler
+
+HF_TOKEN = "your_own_hf_token"
+
+# Load model and scaler
+model_path = hf_hub_download(
+    repo_id="EsferSami/DataSynthis_ML_JobTask",
+    filename="Apple-Stock-Price-Forecasting-LSTM-Model/apple_stock_lstm.h5",
+    token=HF_TOKEN
+)
+scaler_path = hf_hub_download(
+    repo_id="EsferSami/DataSynthis_ML_JobTask",
+    filename="Apple-Stock-Price-Forecasting-LSTM-Model/scaler.joblib",
+    token=HF_TOKEN
+)
+
+model = tf.keras.models.load_model(model_path)
+scaler = joblib.load(scaler_path)
+
+# Download recent data
+data = yf.download("AAPL", period="3mo", auto_adjust=False)
+recent_prices = data['Adj Close'].values.astype(float)
+
+# Prepare input
+last_60_days = recent_prices[-60:].reshape(-1, 1)
+last_60_scaled = scaler.transform(last_60_days)
+
+predictions = []
+current_seq = last_60_scaled.copy()
+last_price = last_60_days[-1][0]
+MAX_DAILY_CHANGE = 0.02
+
+for day in range(7):
+    input_data = current_seq.reshape(1, 60, 1)
+    pred_scaled = model.predict(input_data, verbose=0)
+    pred_price_raw = scaler.inverse_transform(pred_scaled)[0][0]
+    
+    change = pred_price_raw - last_price
+    change = np.clip(change, -MAX_DAILY_CHANGE*last_price, MAX_DAILY_CHANGE*last_price)
+    anchored_price = last_price + change
+    predictions.append(anchored_price)
+    
+    pred_scaled_reshaped = scaler.transform(np.array([[anchored_price]]))
+    current_seq = np.append(current_seq[1:], pred_scaled_reshaped, axis=0)
+    last_price = anchored_price
+
+prediction_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=7)
+results_df = pd.DataFrame({'Date': prediction_dates, 'Predicted_Price': np.round(predictions, 2)})
+
+print("\nLSTM - 7-Day Forecast")
+print("="*50)
+print(results_df.to_string(index=False))
+</details>